LASPATED: A Library for the Analysis of Spatio-Temporal Discrete Data (User Manual)
Vincent Guigues, Anton J. Kleywegt, Giovanni Amorim, Andre Krauss, Victor Hugo Nascimento

TL;DR
LASPATED is a comprehensive Python library with supplementary Matlab and C++ packages designed for analyzing and calibrating probabilistic models of stochastic spatio-temporal data, facilitating research and application in this domain.
Contribution
This paper introduces LASPATED, a new software library that provides tools for analyzing and calibrating models of stochastic spatio-temporal data, with implementations across multiple programming languages.
Findings
Library available on GitHub with extensive tools.
Includes calibration methods for probabilistic models.
Provides tutorials and documentation for users.
Abstract
This is the User Manual of the LASPATED library. This library is available on GitHub (at https://github.com/vguigues/LASPATED)) and provides a set of tools to analyze spatiotemporal data. A video tutorial for this library is available on Youtube. It is made of a Python package for time and space discretizations and of two packages (one in Matlab and one in C++) implementing the calibration of the probabilistic models for stochastic spatio-temporal data proposed in the companion paper arXiv:2203.16371v2.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Management and Algorithms · Geographic Information Systems Studies
